app.py

import gradio as gr
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch

# Load CLIP
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Prompt template
def generate_caption(image):
    inputs = clip_processor(images=image, return_tensors="pt")
    outputs = clip_model.get_image_features(**inputs)
    
    # Convert image features into a dummy "caption" using top concept labels
    # (In actual implementation, this could be passed to GPT-like models)
    # Here we simulate a caption
    return "A photo showing something relevant to the content."

demo = gr.Interface(fn=generate_caption,
                    inputs=gr.Image(type="pil"),
                    outputs="text",
                    title="Image Captioning with CLIP & GPT-style Generation",
                    description="Upload an image to get a descriptive caption. Based on CLIP for vision understanding.")

demo.launch()